ml articles that use less than 100 training datapoints [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]}, [17]}, [18]}, [19]} are typically bo and bo/ad techniques [6]}, [7]}, [8]}, [9]}, [13]}, [14]}, [15]}, [17]}, [18]}, with some svm [1]}, [3]}, [5]}, [19]} among others (e.g. sr [16]} and rf [14]}). This is to be expected, as bo and ad techniques can allow fewer experiments to be performed while maximizing the exploratory (probing high uncertainty regions) and exploitative (probing favorable prediction regions) gains of optimization. bo techniques benefit from the inherent availability of uncertainty quantification in addition to property predictions. This can be used for uncertainty quantification through models and offer better explanation of results that deviate from predictions or confirmation of results in areas with low uncertainty and high predictive accuracy. Uncertainty can also be quantified with varying degrees of success for other methods (e.g. bootstrapping svm results [14]}, [36]}, [37]}, [19]}). We now share examples of experimental [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [12]}, [14]}, [15]}, [16]}, [17]}, [18]}, [19]} and computational [1]}, [2]}, [3]}, [11]}, [13]} validation articles, first addressing bo and ad (sec:1-100:bo) followed by those of other ml types (sec:1-100:other).
